https://doi.org/10.4081/jae.2024.1585

Potato powdery scab segmentation using improved GrabCut algorithm

Early Access
Published: 9 May 2024
Disease control, GrabCut, lesion segmentation, potato powdery scab, YOLOv7
Abstract Views: 85
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Authors

Rui Liu
Faculty of Information, Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Tong Zhu
Faculty of Information, Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Jiawei Wu
Faculty of Information, Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.
Jingtao Li
Faculty of Information, Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan, China.

Potato powdery scab is a serious disease that affects potato yield and has widespread global impacts. Due to its concealed symptoms, it is difficult to detect and control the disease once lesions appear. This paper aims to overcome the drawbacks of interactive algorithms and proposes an optimized approach using object detection for the GrabCut algorithm. We design a YOLOv7-guided non-interactive GrabCut algorithm and combine it with image denoising techniques, considering the characteristics of potato powdery scab lesions. We successfully achieve effective segmentation of potato powdery scab lesions. Through experiments, the improved segmentation algorithm has an average accuracy of 88.05%, and the highest accuracy can reach 91.07%. This is an increase of 46.28% and 32.69% respectively compared to the relatively accurate K-means algorithm. Moreover, compared to the original algorithm which could not segment the lesions independently, the improvement is more significant. The experimental results indicate that the algorithm has a high segmentation accuracy, which provides strong support for further disease analysis and control.

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How to Cite

Liu, R., Zhu, T., Wu, J. and Li, J. (2024) “Potato powdery scab segmentation using improved GrabCut algorithm”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1585.
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